1
|
Adhikari P, Jawad B, Ching WY. Electronic Interactions Between the Receptor-Binding Domain of Omicron Variants and Angiotensin-Converting Enzyme 2: A Novel Amino Acid-Amino Acid Bond Pair Concept. Molecules 2025; 30:2061. [PMID: 40363865 PMCID: PMC12073306 DOI: 10.3390/molecules30092061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2025] [Revised: 04/29/2025] [Accepted: 05/01/2025] [Indexed: 05/15/2025] Open
Abstract
SARS-CoV-2 remains a severe threat to worldwide public health, particularly as the virus continues to evolve and diversify into variants of concern (VOCs). Among these VOCs, Omicron variants exhibit unique phenotypic traits, such as immune evasion, transmissibility, and severity, due to numerous spike protein mutations and the rapid subvariant evolution. These Omicron subvariants have more than 15 mutations in the receptor-binding domain (RBD), a region of the SARS-CoV-2 spike protein that is important for recognition and binding with the angiotensin-converting enzyme 2 (ACE2) human receptor. To address the impact of these high numbers of Omicron mutations on the binding process, we have developed a novel method to precisely quantify amino acid interactions via the amino acid-amino acid bond pair (AABP). We applied this concept to investigate the interface interactions of the RBD-ACE2 complex in four Omicron Variants (BA.1, BA.2, BA.5, and XBB.1.16) with its Wild Type counterpart. Based on the AABP analysis, we have identified all the sites that are affected by mutation and have provided evidence that unmutated sites are also impacted by mutation. We have calculated that the binding between RBD and ACE2 is strongest in OV BA.1, followed by OV BA.2, WT, OV BA.5, and OV XBB.1.16. We also present the partial charge values for all 311 residues across these five models. Our analysis provides a detailed understanding of changes caused by mutation in each Omicron interface complex.
Collapse
Affiliation(s)
- Puja Adhikari
- Department of Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO 64110, USA; (B.J.); (W.-Y.C.)
| | - Bahaa Jawad
- Department of Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO 64110, USA; (B.J.); (W.-Y.C.)
- College of Applied Sciences, University of Technology, Baghdad 10066, Iraq
| | - Wai-Yim Ching
- Department of Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO 64110, USA; (B.J.); (W.-Y.C.)
| |
Collapse
|
2
|
Tilajka Á, Kurilla A, László L, Lovrics A, Novák J, Takács T, Buday L, Vas V. Predictive value analysis of the interaction network of Tks4 scaffold protein in colon cancer. Front Mol Biosci 2024; 11:1414805. [PMID: 39234565 PMCID: PMC11371697 DOI: 10.3389/fmolb.2024.1414805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/31/2024] [Indexed: 09/06/2024] Open
Abstract
Background Colorectal carcinoma (CRC) has emerged as one of the most widespread cancers and was the third leading cause of cancer-related mortality in 2020. The role of the podosomal protein Tks4 in tumor formation and progression is well established, including its involvement in gastric carcinoma and hepatocellular carcinoma; however, exploration of Tks4 and its associated EMT-regulating interactome in the context of colon cancer remains largely unexplored. Methods We conducted a comprehensive bioinformatic analysis to investigate the mRNA and protein expression levels of Tks4 and its associated partner molecules (CD2AP, GRB2, WASL, SRC, CTTN, and CAPZA1) across different tumor types. We quantified the expression levels of Tks4 and its partner molecules using qPCR, utilizing a TissueScan colon cancer array. We then validated the usefulness of Tks4 and its associated molecules as biomarkers via careful statistical analyses, including Pearson's correlation analysis, principal component analysis (PCA), multiple logistic regression, confusion matrix analysis, and ROC analysis. Results Our findings indicate that the co-expression patterns of the seven examined biomarker candidates better differentiate between tumor and normal samples compared with the expression levels of the individual genes. Moreover, variable importance analysis of these seven genes revealed four core genes that yield consistent results similar to the seven genes. Thus, these four core genes from the Tks4 interactome hold promise as potential combined biomarkers for colon adenocarcinoma diagnosis and prognosis. Conclusion Our proposed biomarker set from the Tks4 interactome shows promising sensitivity and specificity, aiding in colon cancer prevention and diagnosis.
Collapse
Affiliation(s)
- Álmos Tilajka
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Anita Kurilla
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Loretta László
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Anna Lovrics
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Julianna Novák
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| | - Tamás Takács
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Doctoral School of Biology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - László Buday
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Virag Vas
- Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary
| |
Collapse
|
3
|
Tang T, Zhang X, Li W, Wang Q, Liu Y, Cao X. Co-training based prediction of multi-label protein-protein interactions. Comput Biol Med 2024; 177:108623. [PMID: 38788374 DOI: 10.1016/j.compbiomed.2024.108623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
Prediction of protein-protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein-Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain.
Collapse
Affiliation(s)
- Tao Tang
- School of Modern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Nanjing, 210023, Jiangsu, China
| | - Xiaocai Zhang
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore, 138632, Singapore
| | - Weizhuo Li
- School of Modern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Nanjing, 210023, Jiangsu, China
| | - Qing Wang
- School of Management, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Nanjing, 210023, Jiangsu, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, 2 Lushan Rd, Changsha, 410086, Hunan, China; Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.
| | - Xiaofeng Cao
- School of Artificial Intelligence, Jilin University, 2699 Qianjin St, Jilin, 130012, Changchun, China
| |
Collapse
|
4
|
Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
Collapse
Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | | |
Collapse
|
5
|
Wu S, Wagner G. Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets. STAR Protoc 2022; 3:101880. [PMID: 36595939 PMCID: PMC9768376 DOI: 10.1016/j.xpro.2022.101880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/08/2022] [Accepted: 11/02/2022] [Indexed: 12/14/2022] Open
Abstract
Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package "eIF4F.analysis". We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).1.
Collapse
Affiliation(s)
- Su Wu
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA,Corresponding author
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA,Corresponding author
| |
Collapse
|
6
|
Li Z, Wang W, Ma B, Yin J, Hu C, Luo P, Wang Y. Genomic and biological characteristics of a newly isolated lytic bacteriophage PZJ0206 infecting the Enterobacter cloacae. Virus Res 2022; 316:198800. [DOI: 10.1016/j.virusres.2022.198800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 04/26/2022] [Accepted: 05/06/2022] [Indexed: 10/18/2022]
|
7
|
Evans-Yamamoto D, Rouleau FD, Nanda P, Makanae K, Liu Y, Després P, Matsuo H, Seki M, Dubé AK, Ascencio D, Yachie N, Landry C. OUP accepted manuscript. Nucleic Acids Res 2022; 50:e54. [PMID: 35137167 PMCID: PMC9122585 DOI: 10.1093/nar/gkac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/22/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.
Collapse
Affiliation(s)
- Daniel Evans-Yamamoto
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa, 252-0882, Japan
- Institute for Advanced Biosciences, Keio University, Fujisawa, 252-0882, Japan
| | - François D Rouleau
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Piyush Nanda
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Koji Makanae
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Yin Liu
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Philippe C Després
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Hitoshi Matsuo
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Motoaki Seki
- Synthetic Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Diana Ascencio
- Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada
- Regroupement Québécois de Recherche sur la Fonction, l’Ingénierie et les Applications des Protéines, (PROTEO), Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biochimie, microbiologie et bio-informatique, Université Laval, Québec, QC, G1V 0A6, Canada
- Département de biologie, Université Laval, Québec, QC, G1V 0A6, Canada
| | - Nozomu Yachie
- Correspondence may also be addressed to Nozomu Yachie. Tel: +1 604 822 9512;
| | - Christian R Landry
- To whom correspondence should be addressed. Tel: +1 418 656 3954; Fax: +1 418 656 7176;
| |
Collapse
|
8
|
Farooq QUA, Shaukat Z, Aiman S, Li CH. Protein-protein interactions: Methods, databases, and applications in virus-host study. World J Virol 2021; 10:288-300. [PMID: 34909403 PMCID: PMC8641042 DOI: 10.5501/wjv.v10.i6.288] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/19/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023] Open
Abstract
Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
Collapse
Affiliation(s)
- Qurat ul Ain Farooq
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Sara Aiman
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Chun-Hua Li
- Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing 100124, China
| |
Collapse
|
9
|
Grigoriev A. Transfer RNA and Origins of RNA Interference. Front Mol Biosci 2021; 8:708984. [PMID: 34368233 PMCID: PMC8343393 DOI: 10.3389/fmolb.2021.708984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/08/2021] [Indexed: 01/21/2023] Open
Affiliation(s)
- Andrey Grigoriev
- Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NY, Uinted States
| |
Collapse
|
10
|
Spatiotemporal 22q11.21 Protein Network Implicates DGCR8-Dependent MicroRNA Biogenesis as a Risk for Late-Fetal Cortical Development in Psychiatric Diseases. Life (Basel) 2021; 11:life11060514. [PMID: 34073122 PMCID: PMC8227527 DOI: 10.3390/life11060514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 12/28/2022] Open
Abstract
The chromosome 22q11.21 copy number variant (CNV) is a vital risk factor that can be a genetic predisposition to neurodevelopmental disorders (NDD). As the 22q11.21 CNV affects multiple genes, causal disease genes and mechanisms affected are still poorly understood. Thus, we aimed to identify the most impactful 22q11.21 CNV genes and the potential impacted human brain regions, developmental stages and signaling pathways. We constructed the spatiotemporal dynamic networks of 22q11.21 CNV genes using the brain developmental transcriptome and physical protein–protein interactions. The affected brain regions, developmental stages, driver genes and pathways were subsequently investigated via integrated bioinformatics analysis. As a result, we first identified that 22q11.21 CNV genes affect the cortical area mainly during late fetal periods. Interestingly, we observed that connections between a driver gene, DGCR8, and its interacting partners, MECP2 and CUL3, also network hubs, only existed in the network of the late fetal period within the cortical region, suggesting their functional specificity during brain development. We also confirmed the physical interaction result between DGCR8 and CUL3 by liquid chromatography-tandem mass spectrometry. In conclusion, our results could suggest that the disruption of DGCR8-dependent microRNA biogenesis plays a vital role in NDD for late fetal cortical development.
Collapse
|
11
|
Fluorescence resonance energy transfer in revealing protein-protein interactions in living cells. Emerg Top Life Sci 2021; 5:49-59. [PMID: 33856021 DOI: 10.1042/etls20200337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 02/22/2021] [Accepted: 03/04/2021] [Indexed: 11/17/2022]
Abstract
Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein-protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein-protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein-protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein-protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein-protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.
Collapse
|
12
|
Kandel A, Dhillon SK, Prabaharan CB, Fatnin Binti Hisham S, Rajamanickam K, Napper S, Chidambaram SB, Essa MM, Yang J, Sakharkar MK. Identifying kinase targets of PPARγ in human breast cancer. J Drug Target 2021; 29:660-668. [PMID: 33496213 DOI: 10.1080/1061186x.2021.1877719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Breast cancer is the most common cancer in women. Despite advances in screening women for genetic predisposition to breast cancer and risk stratification, a majority of women carriers remain undetected until they become affected. Thus, there is a need to develop a cost-effective, rapid, sensitive and non-invasive early-stage diagnostic method. Kinases are involved in all fundamental cellular processes and mutations in kinases have been reported as drivers of cancer. PPARγ is a ligand-activated transcription factor that plays important roles in cell proliferation and metabolism. However, the complete set of kinases modulated by PPARγ is still unknown. In this study, we identified human kinases that are potential PPARγ targets and evaluated their differential expression and gene pair correlations in human breast cancer patient dataset TCGA-BRCA. We further confirmed the findings in human breast cancer cell lines MCF7 and SK-BR-3 using a kinome array. We observed that gene pair correlations are lost in tumours as compared to healthy controls and could be used as a supplement strategy for diagnosis and prognosis of breast cancer.
Collapse
Affiliation(s)
- Anish Kandel
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Sarinder Kaur Dhillon
- Faculty of Science, Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia
| | - Chandra Bose Prabaharan
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | | | - Karthic Rajamanickam
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Scott Napper
- Vaccine and Infectious Disease Organization-International Vaccine Research Centre, University of Saskatchewan, Saskatoon, Canada.,Department of Biochemistry, College of Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Saravana Babu Chidambaram
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research (JSSAHER), Mysuru, India
| | - Musthafa Mohamed Essa
- Ageing and Dementia Research Group, Sultan Qaboos University, Muscat, Oman.,Department of Food Science and Nutrition, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
| | - Jian Yang
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| | - Meena Kishore Sakharkar
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada
| |
Collapse
|
13
|
Chen Q, Li Y, Tan K, Qiao Y, Pan S, Jiang T, Chen YPP. Network-based methods for gene function prediction. Brief Funct Genomics 2021; 20:249-257. [PMID: 33686431 DOI: 10.1093/bfgp/elab006] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 12/23/2022] Open
Abstract
The rapid development of high-throughput technology has generated a large number of biological networks. Network-based methods are able to provide rich information for inferring gene function. This is composed of analyzing the topological characteristics of genes in related networks, integrating biological information, and considering data from different data sources. To promote network biology and related biotechnology research, this article provides a survey for the state of the art of advanced methods of network-based gene function prediction and discusses the potential challenges.
Collapse
Affiliation(s)
- Qingfeng Chen
- University of Technology Sydney, China and Hundred-Talent Program
| | - Yongjie Li
- School of Computer and Electronic Information at Guangxi University
| | - Kai Tan
- School of Computer and Electronic Information at Guangxi University
| | - Yvlu Qiao
- School of Computer and Electronic Information at Guangxi University
| | - Shirui Pan
- Computer science from the University of Technology Sydney
| | - Taijiao Jiang
- Suzhou Institute of System Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia
| |
Collapse
|
14
|
Cope AL, O'Meara BC, Gilchrist MA. Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods. BMC Genomics 2020; 21:370. [PMID: 32434474 PMCID: PMC7240986 DOI: 10.1186/s12864-020-6761-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/29/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. RESULTS We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. CONCLUSIONS Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.
Collapse
Affiliation(s)
- Alexander L Cope
- Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA.
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA.
| | - Brian C O'Meara
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA
- National Institute of Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, USA
| | - Michael A Gilchrist
- Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA
- National Institute of Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, USA
| |
Collapse
|
15
|
Nathan KG, Lal SK. The Multifarious Role of 14-3-3 Family of Proteins in Viral Replication. Viruses 2020; 12:E436. [PMID: 32294919 PMCID: PMC7232403 DOI: 10.3390/v12040436] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 02/06/2023] Open
Abstract
The 14-3-3 proteins are a family of ubiquitous and exclusively eukaryotic proteins with an astoundingly significant number of binding partners. Their binding alters the activity, stability, localization, and phosphorylation state of a target protein. The association of 14-3-3 proteins with the regulation of a wide range of general and specific signaling pathways suggests their crucial role in health and disease. Recent studies have linked 14-3-3 to several RNA and DNA viruses that may contribute to the pathogenesis and progression of infections. Therefore, comprehensive knowledge of host-virus interactions is vital for understanding the viral life cycle and developing effective therapeutic strategies. Moreover, pharmaceutical research is already moving towards targeting host proteins in the control of virus pathogenesis. As such, targeting the right host protein to interrupt host-virus interactions could be an effective therapeutic strategy. In this review, we generated a 14-3-3 protein interactions roadmap in viruses, using the freely available Virusmentha network, an online virus-virus or virus-host interaction tool. Furthermore, we summarize the role of the 14-3-3 family in RNA and DNA viruses. The participation of 14-3-3 in viral infections underlines its significance as a key regulator for the expression of host and viral proteins.
Collapse
Affiliation(s)
- Kavitha Ganesan Nathan
- School of Science, Monash University, Bandar Sunway, Selangor Darul Ehsan 47500, Malaysia;
| | - Sunil K. Lal
- School of Science, Monash University, Bandar Sunway, Selangor Darul Ehsan 47500, Malaysia;
- Tropical Medicine & Biology Platform, Monash University, Bandar Sunway, Selangor Darul Ehsan 47500, Malaysia
| |
Collapse
|
16
|
Li X, Li W, Zeng M, Zheng R, Li M. Network-based methods for predicting essential genes or proteins: a survey. Brief Bioinform 2020; 21:566-583. [PMID: 30776072 DOI: 10.1093/bib/bbz017] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 01/21/2019] [Accepted: 01/22/2019] [Indexed: 01/03/2025] Open
Abstract
Genes that are thought to be critical for the survival of organisms or cells are called essential genes. The prediction of essential genes and their products (essential proteins) is of great value in exploring the mechanism of complex diseases, the study of the minimal required genome for living cells and the development of new drug targets. As laboratory methods are often complicated, costly and time-consuming, a great many of computational methods have been proposed to identify essential genes/proteins from the perspective of the network level with the in-depth understanding of network biology and the rapid development of biotechnologies. Through analyzing the topological characteristics of essential genes/proteins in protein-protein interaction networks (PINs), integrating biological information and considering the dynamic features of PINs, network-based methods have been proved to be effective in the identification of essential genes/proteins. In this paper, we survey the advanced methods for network-based prediction of essential genes/proteins and present the challenges and directions for future research.
Collapse
Affiliation(s)
- Xingyi Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Wenkai Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| |
Collapse
|
17
|
Sabir JSM, El Omri A, Banaganapalli B, Aljuaid N, Omar AMS, Altaf A, Hajrah NH, Zrelli H, Arfaoui L, Elango R, Alharbi MG, Alhebshi AM, Jansen RK, Shaik NA, Khan M. Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis. PLoS One 2020; 15:e0228400. [PMID: 32027667 PMCID: PMC7004317 DOI: 10.1371/journal.pone.0228400] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023] Open
Abstract
Obesity is a multifactorial disease caused by complex interactions between genes and dietary factors. Salt-rich diet is related to the development and progression of several chronic diseases including obesity. However, the molecular basis of how salt sensitivity genes (SSG) contribute to adiposity in obesity patients remains unexplored. In this study, we used the microarray expression data of visceral adipose tissue samples and constructed a complex protein-interaction network of salt sensitivity genes and their co-expressed genes to trace the molecular pathways connected to obesity. The Salt Sensitivity Protein Interaction Network (SSPIN) of 2691 differentially expressed genes and their 15474 interactions has shown that adipose tissues are enriched with the expression of 23 SSGs, 16 hubs and 84 bottlenecks (p = 2.52 x 10-16) involved in diverse molecular pathways connected to adiposity. Fifteen of these 23 SSGs along with 8 other SSGs showed a co-expression with enriched obesity-related genes (r ≥ 0.8). These SSGs and their co-expression partners are involved in diverse metabolic pathways including adipogenesis, adipocytokine signaling pathway, renin-angiotensin system, etc. This study concludes that SSGs could act as molecular signatures for tracing the basis of adipogenesis among obese patients. Integrated network centered methods may accelerate the identification of new molecular targets from the complex obesity genomics data.
Collapse
Affiliation(s)
- Jamal Sabir M. Sabir
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdelfatteh El Omri
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nada Aljuaid
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulkader M. Shaikh Omar
- Biology, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulmalik Altaf
- Department of Surgery, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nahid H. Hajrah
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Houda Zrelli
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Leila Arfaoui
- Clinical Nutrition Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ramu Elango
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mona G. Alharbi
- Biology, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Alawiah M. Alhebshi
- Biology, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Robert K. Jansen
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States of America
| | - Noor A. Shaik
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhummadh Khan
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- * E-mail:
| |
Collapse
|
18
|
Zhang J, Ju S. Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method. IET Syst Biol 2019; 13:290-296. [PMID: 31778125 PMCID: PMC8687158 DOI: 10.1049/iet-syb.2019.0060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/24/2019] [Accepted: 07/09/2019] [Indexed: 11/20/2022] Open
Abstract
Direct relationships between biological molecules connected in a gene co-expression network tend to reflect real biological activities such as gene regulation, protein-protein interactions (PPIs), and metabolisation. As correlation-based networks contain numerous indirect connections, those direct relationships are always 'hidden' in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co-expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a 'deconvolution filter' contain more genuine PPIs than before deconvolution.
Collapse
Affiliation(s)
- Jin Zhang
- School of Information Science and Engineering, University of Jinan, Jinan 250022, People's Republic of China.
| | - Shan Ju
- School of International Trade and Economics, Shandong University of Finance and Economics, Jinan 250014, People's Republic of China
| |
Collapse
|
19
|
Konda AK, Sabale PR, Soren KR, Subramaniam SP, Singh P, Rathod S, Chaturvedi SK, Singh NP. Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190204152500] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Chickpea is a nutritional rich premier pulse crop but its production encounters
setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance.
Objective:
The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea
in response to herbicide stress and decipher their interacting partners.
Methods:
For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior
of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional
gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis
was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed
in chickpea using publicly available transcriptome data. Expression pattern of the identified gene
network was studied in chickpea-Fusarium interactions.
Results:
A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not
only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely
Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620,
Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes
based on the significant probability scores. Functional annotation indicated role in callose deposition and
response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in
wilt resistant and susceptible chickpea genotypes, infected with Fusarium.
Conclusion:
This is the first report of identification of a multi-stress responsive WRKY TF and its associated
GCN in chickpea.
Collapse
Affiliation(s)
| | | | - Khela R. Soren
- ICAR-Indian Institute of Pulses Research, Kanpur, 208024, India
| | | | - Pallavi Singh
- ICAR-Indian Institute of Pulses Research, Kanpur, 208024, India
| | - Santosh Rathod
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi- 110012, India
| | | | | |
Collapse
|
20
|
Guala D, Ogris C, Müller N, Sonnhammer ELL. Genome-wide functional association networks: background, data & state-of-the-art resources. Brief Bioinform 2019; 21:1224-1237. [PMID: 31281921 PMCID: PMC7373183 DOI: 10.1093/bib/bbz064] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/29/2019] [Accepted: 05/04/2019] [Indexed: 02/06/2023] Open
Abstract
The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
Collapse
Affiliation(s)
- Dimitri Guala
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Christoph Ogris
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Nikola Müller
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Erik L L Sonnhammer
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| |
Collapse
|
21
|
Zhao J, Lei Y, Hong J, Zheng C, Zhang L. AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in Arabidopsis thaliana. FRONTIERS IN PLANT SCIENCE 2019; 10:870. [PMID: 31333706 PMCID: PMC6625390 DOI: 10.3389/fpls.2019.00870] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 06/18/2019] [Indexed: 05/29/2023]
Abstract
Protein-protein interactions (PPIs) play fundamental roles in various cellular processes. Here, we present a new version of computational interactome that contains more than 345,000 predicted PPIs involving about 51.2% of the Arabidopsis proteins. Compared to the earlier version, the updated AraPPINet displays a higher accuracy in predicting protein interactions through performance evaluation with independent datasets. In addition to the experimental verifications of the previous version, the new version has been subjected to further validation test that demonstrates its ability to discover novel PPIs involved in hormone signaling pathways. Moreover, network analysis shows that many overlapping proteins are significantly involved in the interactions which mediated the crosstalk among plant hormones. The new version of AraPPINet provides a more reliable interactome which would facilitate the understanding of crosstalk among hormone signaling pathways in plants.
Collapse
|
22
|
Rasti S, Vogiatzis C. A survey of computational methods in protein–protein interaction networks. ANNALS OF OPERATIONS RESEARCH 2019; 276:35-87. [DOI: 10.1007/s10479-018-2956-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
|
23
|
Sabir JSM, El Omri A, Shaik NA, Banaganapalli B, Al-Shaeri MA, Alkenani NA, Hajrah NH, Awan ZA, Zrelli H, Elango R, Khan M. Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network. PLoS One 2019; 14:e0214337. [PMID: 31013288 PMCID: PMC6478283 DOI: 10.1371/journal.pone.0214337] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 03/11/2019] [Indexed: 12/12/2022] Open
Abstract
Obesity is connected to the activation of chronic inflammatory pathways in both adipocytes and macrophages located in adipose tissues. The nuclear factor (NF)-κB is a central molecule involved in inflammatory pathways linked to the pathology of different complex metabolic disorders. Investigating the gene expression data in the adipose tissue would potentially unravel disease relevant gene interactions. The present study is aimed at creating a signature molecular network and at prioritizing the potential biomarkers interacting with NF-κB family of proteins in obesity using system biology approaches. The dataset GSE88837 associated with obesity was downloaded from Gene Expression Omnibus (GEO) database. Statistical analysis represented the differential expression of a total of 2650 genes in adipose tissues (p = <0.05). Using concepts like correlation, semantic similarity, and theoretical graph parameters we narrowed down genes to a network of 23 genes strongly connected with NF-κB family with higher significance. Functional enrichment analysis revealed 21 of 23 target genes of NF-κB were found to have a critical role in the pathophysiology of obesity. Interestingly, GEM and PPP1R13L were predicted as novel genes which may act as potential target or biomarkers of obesity as they occur with other 21 target genes with known obesity relationship. Our study concludes that NF-κB and prioritized target genes regulate the inflammation in adipose tissues through several molecular signaling pathways like NF-κB, PI3K-Akt, glucocorticoid receptor regulatory network, angiogenesis and cytokine pathways. This integrated system biology approaches can be applied for elucidating functional protein interaction networks of NF-κB protein family in different complex diseases. Our integrative and network-based approach for finding therapeutic targets in genomic data could accelerate the identification of novel drug targets for obesity.
Collapse
Affiliation(s)
- Jamal S. M. Sabir
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King abdulaziz University, Jeddah, Saudi Arabia
| | - Abdelfatteh El Omri
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King abdulaziz University, Jeddah, Saudi Arabia
- * E-mail: (MK); (AEO)
| | - Noor A. Shaik
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Majed A. Al-Shaeri
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Naser A. Alkenani
- Biology- Zoology Division, Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nahid H. Hajrah
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King abdulaziz University, Jeddah, Saudi Arabia
| | - Zuhier A. Awan
- Department of Clinical Biochemistry. Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Houda Zrelli
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King abdulaziz University, Jeddah, Saudi Arabia
| | - Ramu Elango
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Muhummadh Khan
- Center of Excellence in Bionanoscience Research, King Abdulaziz University, Jeddah, Saudi Arabia
- Genomics and Biotechnology Section and Research Group, Department of Biological Sciences, Faculty of Science, King abdulaziz University, Jeddah, Saudi Arabia
- * E-mail: (MK); (AEO)
| |
Collapse
|
24
|
Nam Y, Jhee JH, Cho J, Lee JH, Shin H. Disease gene identification based on generic and disease-specific genome networks. Bioinformatics 2018; 35:1923-1930. [DOI: 10.1093/bioinformatics/bty882] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 10/11/2018] [Accepted: 10/17/2018] [Indexed: 01/11/2023] Open
Affiliation(s)
- Yonghyun Nam
- Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea
| | - Jong Ho Jhee
- Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea
| | - Junhee Cho
- Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea
| | - Ji-Hyun Lee
- DR. Noah Biotech, Yeongtong-gu, Suwon, South Korea
| | - Hyunjung Shin
- Department of Industrial Engineering, Ajou University, Yeongtong-gu, Suwon, South Korea
| |
Collapse
|
25
|
Zhang L, Liu JY, Gu H, Du Y, Zuo JF, Zhang Z, Zhang M, Li P, Dunwell JM, Cao Y, Zhang Z, Zhang YM. Bradyrhizobium diazoefficiens USDA 110- Glycine max Interactome Provides Candidate Proteins Associated with Symbiosis. J Proteome Res 2018; 17:3061-3074. [PMID: 30091610 DOI: 10.1021/acs.jproteome.8b00209] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Although the legume-rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein-protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max-B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean.
Collapse
Affiliation(s)
- Li Zhang
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
- School of Public Health , Xinxiang Medical University , Xinxiang 453003 , China
| | - Jin-Yang Liu
- College of Agriculture, Nanjing Agricultural University , Nanjing 210095 , China
| | - Huan Gu
- College of Agriculture, Nanjing Agricultural University , Nanjing 210095 , China
| | - Yanfang Du
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| | - Jian-Fang Zuo
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| | - Zhibin Zhang
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| | - Menglin Zhang
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| | - Pan Li
- School of Public Health , Xinxiang Medical University , Xinxiang 453003 , China
| | - Jim M Dunwell
- School of Agriculture, Policy and Development , University of Reading , Reading RG6 6AR , United Kingdom
| | - Yangrong Cao
- College of Life Science and Technology , Huazhong Agricultural University , Wuhan 430070 , China
| | - Zuxin Zhang
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| | - Yuan-Ming Zhang
- Crop Information Center , College of Plant Science and Technology, Huazhong Agricultural University , Wuhan 430070 , China
| |
Collapse
|
26
|
Ding Z, Kihara D. Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features. CURRENT PROTOCOLS IN PROTEIN SCIENCE 2018; 93:e62. [PMID: 29927082 PMCID: PMC6097941 DOI: 10.1002/cpps.62] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.
Collapse
Affiliation(s)
- Ziyun Ding
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907 USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907 USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907 USA
- Corresponding author: DK; , Phone: 1-765-496-2284 (DK)
| |
Collapse
|
27
|
Novel insights into global translational regulation through Pumilio family RNA-binding protein Puf3p revealed by ribosomal profiling. Curr Genet 2018; 65:201-212. [PMID: 29951697 DOI: 10.1007/s00294-018-0862-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/16/2018] [Accepted: 06/19/2018] [Indexed: 01/13/2023]
Abstract
RNA binding proteins (RBPs) can regulate the stability, localization, and translation of their target mRNAs. Among them, Puf3p is a well-known Pumilio family RBP whose biology has been intensively studied. Nevertheless, the impact of Puf3p on the translational regulation of its downstream genes still remains to be investigated at the genome-wide level. In this study, we combined ribosome profiling and RNA-Seq in budding yeast (Saccharomyces cerevisiae) to investigate Puf3p's functions in translational regulation. Comparison of translational efficiency (TE) between wild-type and puf3Δ strains demonstrates extensive translational modulation in the absence of Puf3p (over 27% genes are affected at the genome level). Besides confirming its known role in regulating mitochondrial metabolism, our data demonstrate that Puf3p serves as a key post-transcriptional regulator of downstream RBPs by regulating their translational efficiencies, indicating a network of interactions among RBPs at the post-transcriptional level. Furthermore, Puf3p switches the balance of translational flux between mitochondrial and cytosolic ribosome biogenesis to adapt to changes in cellular metabolism. In summary, our results indicate that TE can be utilized as an informative index to interrogate the mechanism underlying RBP functions, and provide novel insights into Puf3p's mode-of-action.
Collapse
|
28
|
Mounika Inavolu S, Renbarger J, Radovich M, Vasudevaraja V, Kinnebrew GH, Zhang S, Cheng L. IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:168-176. [PMID: 28266149 PMCID: PMC5351413 DOI: 10.1002/psp4.12167] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/05/2017] [Accepted: 01/06/2017] [Indexed: 12/18/2022]
Abstract
Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein‐protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal‐A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer‐related genes and the well‐known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple‐negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.
Collapse
Affiliation(s)
- S Mounika Inavolu
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - J Renbarger
- Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - M Radovich
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - V Vasudevaraja
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - G H Kinnebrew
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - S Zhang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - L Cheng
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| |
Collapse
|
29
|
Zhang K, Li Y, Li T, Li ZG, Hsiang T, Zhang Z, Sun W. Pathogenicity Genes in Ustilaginoidea virens Revealed by a Predicted Protein-Protein Interaction Network. J Proteome Res 2017; 16:1193-1206. [PMID: 28099032 DOI: 10.1021/acs.jproteome.6b00720] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Rice false smut, caused by Ustilaginoidea virens, produces significant losses in rice yield and grain quality and has recently emerged as one of the most important rice diseases worldwide. Despite its importance in rice production, relatively few studies have been conducted to illustrate the complex interactome and the pathogenicity gene interactions. Here a protein-protein interaction network of U. virens was built through two well-recognized approaches, interolog- and domain-domain interaction-based methods. A total of 20 217 interactions associated with 3305 proteins were predicted after strict filtering. The reliability of the network was assessed computationally and experimentally. The topology of the interactome network revealed highly connected proteins. A pathogenicity-related subnetwork involving up-regulated genes during early U. virens infection was also constructed, and many novel pathogenicity proteins were predicted in the subnetwork. In addition, we built an interspecies PPI network between U. virens and Oryza sativa, providing new insights for molecular interactions of this host-pathogen pathosystem. A web-based publicly available interactive database based on these interaction networks has also been released. In summary, a proteome-scale map of the PPI network was described for U. virens, which will provide new perspectives for finely dissecting interactions of genes related to its pathogenicity.
Collapse
Affiliation(s)
- Kang Zhang
- Department of Plant Pathology and the Ministry of Agriculture Key Laboratory for Plant Pathology, China Agricultural University , Beijing 100193, China
| | - Yuejiao Li
- Department of Plant Pathology and the Ministry of Agriculture Key Laboratory for Plant Pathology, China Agricultural University , Beijing 100193, China
| | - Tengjiao Li
- Department of Plant Pathology and the Ministry of Agriculture Key Laboratory for Plant Pathology, China Agricultural University , Beijing 100193, China
| | - Zhi-Gang Li
- Department of Plant Pathology and the Ministry of Agriculture Key Laboratory for Plant Pathology, China Agricultural University , Beijing 100193, China
| | - Tom Hsiang
- School of Environmental Sciences, University of Guelph , Guelph, Ontario N1G 2W1, Canada
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University , Beijing 100193, China
| | - Wenxian Sun
- Department of Plant Pathology and the Ministry of Agriculture Key Laboratory for Plant Pathology, China Agricultural University , Beijing 100193, China
| |
Collapse
|
30
|
Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol 2017; 1484:237-253. [PMID: 27787830 DOI: 10.1007/978-1-4939-6406-2_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.
Collapse
|
31
|
Jain S, Bader GD. Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast. Bioinformatics 2016; 32:1865-72. [PMID: 26861823 PMCID: PMC4908317 DOI: 10.1093/bioinformatics/btw045] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 12/05/2015] [Accepted: 01/20/2016] [Indexed: 12/02/2022] Open
Abstract
MOTIVATION Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein-protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins. RESULTS A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein-protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97. AVAILABILITY AND IMPLEMENTATION Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred The DoMo-Pred command line tool is implemented using Python 2.7 and C ++. CONTACT gary.bader@utoronto.ca SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Shobhit Jain
- Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Gary D Bader
- Department of Computer Science and The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
32
|
Zhang F, Liu S, Li L, Zuo K, Zhao L, Zhang L. Genome-Wide Inference of Protein-Protein Interaction Networks Identifies Crosstalk in Abscisic Acid Signaling. PLANT PHYSIOLOGY 2016; 171:1511-22. [PMID: 27208273 PMCID: PMC4902594 DOI: 10.1104/pp.16.00057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 04/14/2016] [Indexed: 05/24/2023]
Abstract
Protein-protein interactions (PPIs) are essential to almost all cellular processes. To better understand the relationships of proteins in Arabidopsis (Arabidopsis thaliana), we have developed a genome-wide protein interaction network (AraPPINet) that is inferred from both three-dimensional structures and functional evidence and that encompasses 316,747 high-confidence interactions among 12,574 proteins. AraPPINet exhibited high predictive power for discovering protein interactions at a 50% true positive rate and for discriminating positive interactions from similar protein pairs at a 70% true positive rate. Experimental evaluation of a set of predicted PPIs demonstrated the ability of AraPPINet to identify novel protein interactions involved in a specific process at an approximately 100-fold greater accuracy than random protein-protein pairs in a test case of abscisic acid (ABA) signaling. Genetic analysis of an experimentally validated, predicted interaction between ARR1 and PYL1 uncovered cross talk between ABA and cytokinin signaling in the control of root growth. Therefore, we demonstrate the power of AraPPINet (http://netbio.sjtu.edu.cn/arappinet/) as a resource for discovering gene function in converging signaling pathways and complex traits in plants.
Collapse
Affiliation(s)
- Fangyuan Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shiwei Liu
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ling Li
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kaijing Zuo
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lingxia Zhao
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lida Zhang
- Department of Plant Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| |
Collapse
|
33
|
Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution. Sci Rep 2016; 6:26737. [PMID: 27220911 PMCID: PMC4879665 DOI: 10.1038/srep26737] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Accepted: 05/06/2016] [Indexed: 11/08/2022] Open
Abstract
Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions.
Collapse
|
34
|
Bhardwaj J, Gangwar I, Panzade G, Shankar R, Yadav SK. Global De Novo Protein-Protein Interactome Elucidates Interactions of Drought-Responsive Proteins in Horse Gram (Macrotyloma uniflorum). J Proteome Res 2016; 15:1794-809. [PMID: 27161830 DOI: 10.1021/acs.jproteome.5b01114] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Inspired by the availability of de novo transcriptome of horse gram (Macrotyloma uniflorum) and recent developments in systems biology studies, the first ever global protein-protein interactome (PPI) map was constructed for this highly drought-tolerant legume. Large-scale studies of PPIs and the constructed database would provide rationale behind the interplay at cascading translational levels for drought stress-adaptive mechanisms in horse gram. Using a bidirectional approach (interolog and domain-based), a high-confidence interactome map and database for horse gram was constructed. Available transcriptomic information for shoot and root tissues of a sensitive (M-191; genotype 1) and a drought-tolerant (M-249; genotype 2) genotype of horse gram was utilized to draw comparative PPI subnetworks under drought stress. High-confidence 6804 interactions were predicted among 1812 proteins covering about one-fourth of the horse gram proteome. The highest number of interactions (33.86%) in horse gram interactome matched with Arabidopsis PPI data. The top five hub nodes mostly included ubiquitin and heat-shock-related proteins. Higher numbers of PPIs were found to be responsive in shoot tissue (416) and root tissue (2228) of genotype 2 compared with shoot tissue (136) and root tissue (579) of genotype 1. Characterization of PPIs using gene ontology analysis revealed that kinase and transferase activities involved in signal transduction, cellular processes, nucleocytoplasmic transport, protein ubiquitination, and localization of molecules were most responsive to drought stress. Hence, these could be framed in stress adaptive mechanisms of horse gram. Being the first legume global PPI map, it would provide new insights into gene and protein regulatory networks for drought stress tolerance mechanisms in horse gram. Information compiled in the form of database (MauPIR) will provide the much needed high-confidence systems biology information for horse gram genes, proteins, and involved processes. This information would ease the effort and increase the efficacy for similar studies on other legumes. Public access is available at http://14.139.59.221/MauPIR/ .
Collapse
Affiliation(s)
| | | | | | | | - Sudesh Kumar Yadav
- Center of Innovative and Applied Bioprocessing (CIAB) , Mohali 160071, Punjab, India
| |
Collapse
|
35
|
ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity. BIOMED RESEARCH INTERNATIONAL 2015; 2015:213750. [PMID: 26339594 PMCID: PMC4538409 DOI: 10.1155/2015/213750] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 01/16/2015] [Indexed: 01/19/2023]
Abstract
Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein's proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer's disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA.
Collapse
|
36
|
Lin GN, Corominas R, Lemmens I, Yang X, Tavernier J, Hill DE, Vidal M, Sebat J, Iakoucheva LM. Spatiotemporal 16p11.2 protein network implicates cortical late mid-fetal brain development and KCTD13-Cul3-RhoA pathway in psychiatric diseases. Neuron 2015; 85:742-54. [PMID: 25695269 DOI: 10.1016/j.neuron.2015.01.010] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 08/17/2014] [Accepted: 01/14/2015] [Indexed: 12/19/2022]
Abstract
The psychiatric disorders autism and schizophrenia have a strong genetic component, and copy number variants (CNVs) are firmly implicated. Recurrent deletions and duplications of chromosome 16p11.2 confer a high risk for both diseases, but the pathways disrupted by this CNV are poorly defined. Here we investigate the dynamics of the 16p11.2 network by integrating physical interactions of 16p11.2 proteins with spatiotemporal gene expression from the developing human brain. We observe profound changes in protein interaction networks throughout different stages of brain development and/or in different brain regions. We identify the late mid-fetal period of cortical development as most critical for establishing the connectivity of 16p11.2 proteins with their co-expressed partners. Furthermore, our results suggest that the regulation of the KCTD13-Cul3-RhoA pathway in layer 4 of the inner cortical plate is crucial for controlling brain size and connectivity and that its dysregulation by de novo mutations may be a potential determinant of 16p11.2 CNV deletion and duplication phenotypes.
Collapse
Affiliation(s)
- Guan Ning Lin
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Roser Corominas
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Irma Lemmens
- Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA 02215, USA
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - David E Hill
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA 02215, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA 02215, USA
| | - Jonathan Sebat
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; Beyster Center for Genomics of Psychiatric Diseases, University of California San Diego, La Jolla, CA 92093, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
37
|
Anděl M, Kléma J, Krejčík Z. Network-constrained forest for regularized classification of omics data. Methods 2015; 83:88-97. [PMID: 25872185 DOI: 10.1016/j.ymeth.2015.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 04/01/2015] [Accepted: 04/02/2015] [Indexed: 12/28/2022] Open
Abstract
Contemporary molecular biology deals with wide and heterogeneous sets of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that reduces this overfitting with the aid of prior knowledge in the form of a feature interaction network. We illustrate the proposed method in the task of disease classification based on measured mRNA and miRNA profiles complemented by the interaction network composed of the miRNA-mRNA target relations and mRNA-mRNA interactions corresponding to the interactions between their encoded proteins. We demonstrate that the proposed network-constrained forest employs prior knowledge to increase learning bias and consequently to improve classification accuracy, stability and comprehensibility of the resulting model. The experiments are carried out in the domain of myelodysplastic syndrome that we are concerned about in the long term. We validate our approach in the public domain of ovarian carcinoma, with the same data form. We believe that the idea of a network-constrained forest can straightforwardly be generalized towards arbitrary omics data with an available and non-trivial feature interaction network. The proposed method is publicly available in terms of miXGENE system (http://mixgene.felk.cvut.cz), the workflow that implements the myelodysplastic syndrome experiments is presented as a dedicated case study.
Collapse
Affiliation(s)
- Michael Anděl
- Department of Computer Science, Czech Technical University, Technická 2, Prague, Czech Republic.
| | - Jiří Kléma
- Department of Computer Science, Czech Technical University, Technická 2, Prague, Czech Republic.
| | - Zdeněk Krejčík
- Department of Molecular Genetics, Institute of Hematology and Blood Transfusion, U Nemocnice 1, Prague, Czech Republic.
| |
Collapse
|
38
|
Srihari S, Madhamshettiwar PB, Song S, Liu C, Simpson PT, Khanna KK, Ragan MA. Complex-based analysis of dysregulated cellular processes in cancer. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S1. [PMID: 25521701 PMCID: PMC4290683 DOI: 10.1186/1752-0509-8-s4-s1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Background Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation. Results We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer. Conclusions Complexes in core cellular processes display considerable decreases and countering increases in expression, strongly reflective of compensatory mechanisms in cancer. These changes are directed by the concerted action of cooperative and counteractive TFs. Our study highlights the roles of these complexes and TFs and presents several case studies of compensatory processes, thus providing novel insights into cancer mechanisms.
Collapse
|
39
|
Abstract
A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.
Collapse
|
40
|
Kim Y, Jang JH, Choi S, Hwang D. TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information. Bioinformatics 2014; 30:i453-60. [PMID: 25161233 PMCID: PMC4147907 DOI: 10.1093/bioinformatics/btu454] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation: Time-evolving differential protein–protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. Results: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations. Availability and implementation: Source code and sample data files are available at http://sbm.postech.ac.kr/tempi/sources.zip. Contact:seungjin@postech.ac.kr or dhwang@dgist.ac.kr Supplementary information:Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yongsoo Kim
- School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea
| | - Jin-Hyeok Jang
- School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea
| | - Seungjin Choi
- School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea
| | - Daehee Hwang
- School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea School of Interdisciplinary Bioscience and Bioengineering and Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea and Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea
| |
Collapse
|
41
|
Dhal PK, Barman RK, Saha S, Das S. Dynamic modularity of host protein interaction networks in Salmonella Typhi infection. PLoS One 2014; 9:e104911. [PMID: 25144185 PMCID: PMC4140748 DOI: 10.1371/journal.pone.0104911] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 07/17/2014] [Indexed: 01/08/2023] Open
Abstract
Background Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood. Methods We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified. Results We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever. Conclusions Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi.
Collapse
Affiliation(s)
- Paltu Kumar Dhal
- Biomedical Informatics Centre, National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India
| | - Ranjan Kumar Barman
- Biomedical Informatics Centre, National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India
| | - Sudipto Saha
- Bioinformatics Centre, Bose Institute, Kolkata, West Bengal, India
| | - Santasabuj Das
- Biomedical Informatics Centre, National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India; Division of Clinical Medicine, National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India
| |
Collapse
|
42
|
Lei D, Lin R, Yin C, Li P, Zheng A. Global protein-protein interaction network of rice sheath blight pathogen. J Proteome Res 2014; 13:3277-93. [PMID: 24894516 DOI: 10.1021/pr500069r] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Rhizoctonia solani is the major pathogenic fungi of rice sheath blight. It is responsible for the most serious disease of rice (Oryza sativa L.) and causes significant yield losses in rice-growing countries. Identifying the protein-protein interaction (PPI) maps of R. solani can provide insights into the potential pathogenic mechanisms and assign putative functions to unknown genes. Here, we exploited a PPI map of R. solani anastomosis group 1 IA (AG-1 IA) based on the interolog and domain-domain interaction methods. We constructed a core subset of high-confidence protein networks consisting of 6705 interactions among 1773 proteins. The high quality of the network was revealed by comprehensive methods, including yeast two-hybrid experiments. Pathogenic interaction subnetwork, secreted proteins subnetwork, and mitogen-activated protein kinase (MAPK) cascade subnetwork and their interacting partners were constructed and analyzed. Moreover, to exactly predict the pathogenic factors, the expression levels of the interaction proteins were investigated by analyzing RNA sequences that consisted of samples from the entire infection progress. The PPIs offer an exceptionally rich source of data that can be used to understand the gene functions and biological processes of this serious disease at the system level.
Collapse
Affiliation(s)
- Ding Lei
- Rice Research Institute of Sichuan Agricultural University , Chengdu 611130, China
| | | | | | | | | |
Collapse
|
43
|
Effective identification of essential proteins based on priori knowledge, network topology and gene expressions. Methods 2014; 67:325-33. [DOI: 10.1016/j.ymeth.2014.02.016] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Revised: 01/16/2014] [Accepted: 02/11/2014] [Indexed: 11/23/2022] Open
|
44
|
Protein-protein interaction detection: methods and analysis. INTERNATIONAL JOURNAL OF PROTEOMICS 2014; 2014:147648. [PMID: 24693427 PMCID: PMC3947875 DOI: 10.1155/2014/147648] [Citation(s) in RCA: 424] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/26/2013] [Revised: 12/05/2013] [Accepted: 12/20/2013] [Indexed: 12/24/2022]
Abstract
Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
Collapse
|
45
|
Abstract
Rapid development of genomic and proteomic methodologies has provided a wealth of data for deciphering the biomolecular circuitry of a living cell. The main areas of computational research of proteomes outlined in this review are: understanding the system, its features and parameters to help plan the experiments; data integration, to help produce more reliable data sets; visualization and other forms of data representation to simplify interpretation; modeling of the functional regulation; and systems biology. With false-positive rates reaching 50% even in the more reliable data sets, handling the experimental error remains one of the most challenging tasks. Integrative approaches, incorporating results of various genome- and proteome-wide experiments, allow for minimizing the error and bring with them significant predictive power.
Collapse
|
46
|
Srihari S, Raman V, Leong HW, Ragan MA. Evolution and Controllability of Cancer Networks: A Boolean Perspective. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:83-94. [PMID: 26355510 DOI: 10.1109/tcbb.2013.128] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of these genes. A C/C++ implementation of BoolSpace is freely available at: http://www.bioinformatics.org.au/tools-data.
Collapse
|
47
|
Boucher B, Jenna S. Genetic interaction networks: better understand to better predict. Front Genet 2013; 4:290. [PMID: 24381582 PMCID: PMC3865423 DOI: 10.3389/fgene.2013.00290] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Accepted: 11/28/2013] [Indexed: 12/21/2022] Open
Abstract
A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.
Collapse
Affiliation(s)
- Benjamin Boucher
- Laboratory of Integrative Genomics and Cell Signalling, Pharmaqam, Biomed, Department of Chemistry, Université du Québec à Montréal Montréal, QC, Canada
| | - Sarah Jenna
- Laboratory of Integrative Genomics and Cell Signalling, Pharmaqam, Biomed, Department of Chemistry, Université du Québec à Montréal Montréal, QC, Canada
| |
Collapse
|
48
|
Lysis delay and burst shrinkage of coliphage T7 by deletion of terminator Tφ reversed by deletion of early genes. J Virol 2013; 88:2107-15. [PMID: 24335287 DOI: 10.1128/jvi.03274-13] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Bacteriophage T7 terminator Tϕ is a class I intrinsic terminator coding for an RNA hairpin structure immediately followed by oligo(U), which has been extensively studied in terms of its transcription termination mechanism, but little is known about its physiological or regulatory functions. In this study, using a T7 mutant phage, where a 31-bp segment of Tϕ was deleted from the genome, we discovered that deletion of Tϕ from T7 reduces the phage burst size but delays lysis timing, both of which are disadvantageous for the phage. The burst downsizing could directly result from Tϕ deletion-caused upregulation of gene 17.5, coding for holin, among other Tϕ downstream genes, because infection of gp17.5-overproducing Escherichia coli by wild-type T7 phage showed similar burst downsizing. However, the lysis delay was not associated with cellular levels of holin or lysozyme or with rates of phage adsorption. Instead, when allowed to evolve spontaneously in five independent adaptation experiments, the Tϕ-lacking mutant phage, after 27 or 29 passages, recovered both burst size and lysis time reproducibly by deleting early genes 0.5, 0.6, and 0.7 of class I, among other mutations. Deletion of genes 0.5 to 0.7 from the Tϕ-lacking mutant phage decreased expression of several Tϕ downstream genes to levels similar to that of the wild-type phage. Accordingly, phage T7 lysis timing is associated with cellular levels of Tϕ downstream gene products. This suggests the involvement of unknown factor(s) besides the known lysis proteins, lysozyme and holin, and that Tϕ plays a role of optimizing burst size and lysis time during T7 infection. IMPORTANCE Bacteriophages are bacterium-infecting viruses. After producing numerous progenies inside bacteria, phages lyse bacteria using their lysis protein(s) to get out and start a new infection cycle. Normally, lysis is tightly controlled to ensure phage progenies are maximally produced and released at an optimal time. Here, we have discovered that phage T7, besides employing its known lysis proteins, additionally uses its transcription terminator Tϕ to guarantee the optimal lysis of the E. coli host. Tϕ, positioned in the middle of the T7 genome, must be inactivated at least partially to allow for transcription-driven translocation of T7 DNA into hosts and expression of Tϕ downstream but promoter-lacking genes. What role is played by Tϕ before inactivation? Without Tϕ, not only was lysis time delayed but also the number of progenies was reduced in this study. Furthermore, T7 can overcome Tϕ deletion by further deleting some genes, highlighting that a phage has multiple strategies for optimizing lysis.
Collapse
|
49
|
Jüschke C, Dohnal I, Pichler P, Harzer H, Swart R, Ammerer G, Mechtler K, Knoblich JA. Transcriptome and proteome quantification of a tumor model provides novel insights into post-transcriptional gene regulation. Genome Biol 2013; 14:r133. [PMID: 24289286 PMCID: PMC4053992 DOI: 10.1186/gb-2013-14-11-r133] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Accepted: 11/30/2013] [Indexed: 11/25/2022] Open
Abstract
Background Genome‐wide transcriptome analyses have given systems‐level insights into gene regulatory networks. Due to the limited depth of quantitative proteomics, however, our understanding of post‐transcriptional gene regulation and its effects on protein‐complex stoichiometry are lagging behind. Results Here, we employ deep sequencing and the isobaric tag for relative and absolute quantification (iTRAQ) technology to determine transcript and protein expression changes of a Drosophila brain tumor model at near genome‐wide resolution. In total, we quantify more than 6,200 tissue‐specific proteins, corresponding to about 70% of all transcribed protein‐coding genes. Using our integrated data set, we demonstrate that post‐transcriptional gene regulation varies considerably with biological function and is surprisingly high for genes regulating transcription. We combine our quantitative data with protein‐protein interaction data and show that post‐transcriptional mechanisms significantly enhance co‐regulation of protein‐complex subunits beyond transcriptional co‐regulation. Interestingly, our results suggest that only about 11% of the annotated Drosophila protein complexes are co‐regulated in the brain. Finally, we refine the composition of some of these core protein complexes by analyzing the co‐regulation of potential subunits. Conclusions Our comprehensive transcriptome and proteome data provide a valuable resource for quantitative biology and offer novel insights into understanding post‐transcriptional gene regulation in a tumor model.
Collapse
|
50
|
Schleker S, Ananthasubramanian S, Klein‐Seetharaman J, Ganapathiraju MK. Prediction of Intra‐ and Interspecies Protein–Protein Interactions Facilitating Systems Biology Studies. METHODS AND PRINCIPLES IN MEDICINAL CHEMISTRY 2013:21-53. [DOI: 10.1002/9783527648207.ch2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
|